In Gaussian graphical models, the likelihood equations must typically be solved iteratively, for example by iterative proportional scaling. However, this method may not scale well to models with many variables because it involves repeated inversion of large matrices. We present a version of the algorithm which avoids these inversions, resulting in increased speed, in particular when graphs are sparse.

翻译：在高西亚图形模型中,可能性方程式通常必须反复解答,例如,通过迭代比例缩放。然而,这种方法可能不适宜与许多变量的模型相适应,因为它涉及大型矩阵的反复反转。我们提出了一个避免这些反转的算法版本,导致速度加快,特别是当图表稀少时。